Video-based discomfort detection for infants using a Constrained Local Model

Li, C.; Zinger, Svitlana; Ten, W. E. Tjon A

doi:10.1109/iwssip.2016.7502709

Cited by 8 publications

(2 citation statements)

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“…Research in automatic pain recognition began to take off about a decade ago with the research of 1) Brahnam et al [32][33][34][35][36], who explored classifier systems to detect the facial expressions of pain in 204 static images of neonates experiencing stressful stimuli, 2) Barajas-Montiel and Reyes-Gar ıa [37], who explored classifying cry states in 1623 samples, and 3) Pal et al [38], who combined facial features with cry features from 100 samples to recognize different emotional states, including pain. In the last ten years, research in automatic pain detection has continued to focus on these two behavioral indicators of neonatal pain: facial expressions [32][33][34][35][36][39][40][41][42][43][44][45][46] and infant cries [38,47]. Little research to date has made use of the various physiological measures to detect pain [37,[48][49][50], and none that we are aware of have involved neonates.…”

Section: Introductionmentioning

confidence: 99%

Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors

Brahnam

Nanni

McMurtrey

et al. 2020

ACI

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Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

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Section: Introductionmentioning

confidence: 99%

Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors

Brahnam

Nanni

McMurtrey

et al. 2020

ACI

View full text Add to dashboard Cite

show abstract

“…For example, in the context of gastro-esophageal reflux disease (GERD), infants suspected of GERD undergo 24-hour reflux monitoring, and pain monitoring will allow to analyze detailed time relations between pain and reflux to improve diagnosis. Recent work on detecting discomfort [1] and acute pain [2] of infants shows that automatic monitoring can be based on video analysis of facial expressions (cf. [3]).…”

Section: Introductionmentioning

confidence: 99%